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RecoverFormer: End-to-End Recovery Policy for Humanoid Robots Learns to Switch Among Multiple Stabilization Strategies
arXiv cs.RO (Robotics) · 2026年4月28日
AI要約
- •Researchers present RecoverFormer, a fully end-to-end humanoid recovery policy that learns when and how to switch among recovery behaviors—including compensatory stepping, hand-environment contact, and center-of-mass reshaping—while maintaining robust performance under model mismatch.
- •The architecture combines a causal transformer over a 50-step observation history with a latent recovery mode (enabling smooth transitions among distinct recovery strategies) and a contact affordance head (predicting which environmental surfaces like walls, railings, and table edges are beneficial for stabilization).
- •When trained only on open floor and tested on Unitree G1 humanoid in MuJoCo, RecoverFormer transfers zero shot to walled environments, achieving 100% recovery success across 100–300 N pushes and across wall distances from 0.25–1.4m. Under zero-shot dynamics mismatch, it reaches 75.5% at plus +25% mass, 89% under 30 ms latency, 91.5% at low friction, and 99% under compound friction, latency and mass perturbation.
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